Instructions to use d0r1h/led-base-ilc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use d0r1h/led-base-ilc with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("summarization", model="d0r1h/led-base-ilc")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("d0r1h/led-base-ilc") model = AutoModelForSeq2SeqLM.from_pretrained("d0r1h/led-base-ilc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from d0r1h/led-base-ilc: direct link, hf CLI and curl.
- Browser
- Download file 1.29 GB
-
https://huggingface.co/d0r1h/led-base-ilc/resolve/main/optimizer.pt
- Command line
-
hf download hf://d0r1h/led-base-ilc/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/d0r1h/led-base-ilc/resolve/main/optimizer.pt
1.29 GB
- Xet hash:
- 6b2120623e21d5337ae1a5064d2ae4a364d13591ccdd0a08ae15feb69a1f659f
- Size of remote file:
- 1.29 GB
- SHA256:
- 673c68af74967befac35d599116d165cf4fbb8cfb01aa769bccada6c4214f0e2
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